A “space”, as the term is used herein, is a numerical or alphanumerical system for representing information. A “color space” is a numerical or alphanumerical system that can be used to represent color information by various different types of devices, such as computers, digital cameras, printers, etc. Some examples of different color spaces include RGB (red, green blue), YCrCb (luminance, red chrominance, blue chrominance, also called “YUV”), CMYK (cyan, magenta, yellow, black), HSV (hue, saturation, value) and HSL (hue, saturation, luminance).
Color space conversion is a process performed in many devices to convert pixel color data in one color space to pixel color data in a different color space. Television or other video color data may be provided to a computer system in YCrCb color space. YCrCb is the native color space of NTSC, PAL and MPEG. However, for a computer system to display that data, it may be necessary to convert the data to a color space that is compatible with the computer's display capabilities, such as RGB (red, green, blue) color space. RGB is the native color space of many personal computers and workstations. Similarly, a digital camera may capture image data in one color space but may have to convert it into another color space for purposes of displaying images on a the camera's display device or on a computer or for purposes of printing images.
Color space conversion can be problematic in the context multimedia signal processing, particularly where color data needs to be edited, changed, and/or transformed between color spaces and devices in order to be properly visualized. Fundamentally, the problem is one of accurate color management, color reproduction, color matching, color space transformation, gamut mappings, and the proper reproduction of color from memory and stored image data. In this context, the challenge is finding fast, inexpensive and accurate apparatus and methods to color map signals.
Two prior art solutions are known: the multi-dimensional polynomial interpolation function and multi-dimensional lookup-table (LUT). The first solution requires storing in memory polynomial coefficients, whereas in the LUT solution, samples of the mapped signal are stored in memory. Better mapping accuracy requires more polynomial coefficients for the first solution, and more samples for the second solution. An advantage of the polynomial interpolation approach's advantage over the LUT approach is that less memory is required when polynomial interpolation is employed. An advantage of the LUT approach over the polynomial interpolation approach is that its processing time is fixed, whereas the processing time of polynomial interpolation depends on the order of the polynomial. Also, the polynomial approach has limitations in terms of the complexity of mapping that it can estimate.